A review of the use of genetically engineered enzymes in electrochemical biosensors.
ABSTRACT This article gives an overview of the electrochemical biosensors that incorporate genetically modified enzymes. Firstly, the improvements on the sensitivity and selectivity of biosensors that integrate mutated enzymes are summarised. Next, new trends focused on the oriented immobilisation of mutated enzymes through specific functional groups located at their surface are reviewed. Finally, the effect of enzyme mutations on the electron transfer distance and kinetics of electrochemical biosensors is described.
- SourceAvailable from: Alberto del Monte-Martínez[Show abstract] [Hide abstract]
ABSTRACT: In recent year’s novel bioinformatics tools have been developed to design, rationalize, and optimize the classical techniques to covalently attach proteins to solid surfaces. In this sense, the rational design of immobilized derivative (RDID) has become a potent tool to predict the orientation of proteins covalently attached to the support, the reactivity of protein interacting groups, the maximum protein quantity to immobilize, as well as the protein regions involved in adsorption to the support. RDID include an array of algorithms implemented in the computer program RDID1.0 which take into account the properties of the ligand (3D structure, superficial arrangement of protein interacting groups and its reactivity, etc.) as well as the textural properties of the support during the predictions. In silico analyses may help to establish the optimal immobilization conditions and to understand the behavior of immobilized enzymes. RDID application allows the development of optimal immobilized systems with a great performance in enzymatic bioconversion processes. Novel application of RDID strategy to acylase immobilized biocatalysts and site-directed mutagenesis are described in this work.Breaking news in bioinformatics applied to covalent immobilization of acylases biocatalyst, Simposio Internacional de Química 2013 (SIQ´13), Cayo Santa Maria, Villa Clara, Cuba.; 06/2013
- [Show abstract] [Hide abstract]
ABSTRACT: First automated flow based biosensor for binary organophosphate mixtures in milk.•Highly sensitive detection of organophosphate mixtures using genetically modified AChEs B394 and B4.•Artificial neural network enables good validation of modeled set of OP mixtures.•Simple, rapid and sensitive method for milk screening.Sensors and Actuators B Chemical 03/2015; 208. · 3.84 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: A new silica-gel nanospheres (SiO2NPs) composition was formulated, followed by biochemical surface functionalization to examine its potential in urea biosensor development. The SiO2NPs were basically synthesized based on sol-gel chemistry using a modified Stober method. The SiO2NPs surfaces were modified with amine (-NH2) functional groups for urease immobilization in the presence of glutaric acid (GA) cross-linker. The chromoionophore pH-sensitive dye ETH 5294 was physically adsorbed on the functionalized SiO2NPs as pH transducer. The immobilized urease determined urea concentration reflectometrically based on the colour change of the immobilized chromoionophore as a result of the enzymatic hydrolysis of urea. The pH changes on the biosensor due to the catalytic enzyme reaction of immobilized urease were found to correlate with the urea concentrations over a linear response range of 50-500 mM (R2 = 0.96) with a detection limit of 10 mM urea. The biosensor response time was 9 min with reproducibility of less than 10% relative standard deviation (RSD). This optical urea biosensor did not show interferences by Na+, K+, Mg2+ and NH4+ ions. The biosensor performance has been validated using urine samples in comparison with a non-enzymatic method based on the use of p-dimethylaminobenzaldehyde (DMAB) reagent and demonstrated a good correlation between the two different methods (R2 = 0.996 and regression slope of 1.0307). The SiO2NPs-based reflectometric urea biosensor showed improved dynamic linear response range when compared to other nanoparticle-based optical urea biosensors.Sensors 07/2014; 14(7):13186-13209. · 2.05 Impact Factor